{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process Data\n",
    "\n",
    "Convert the raw temperature data files into a form that the VAE model can accept as training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Raw Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>438288.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73.367874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.317912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.952607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>58.228723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73.569022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>88.170157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>119.622822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Observed\n",
       "count  438288.000000\n",
       "mean       73.367874\n",
       "std        18.317912\n",
       "min        21.952607\n",
       "25%        58.228723\n",
       "50%        73.569022\n",
       "75%        88.170157\n",
       "max       119.622822"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list = []\n",
    "for year in range(1974, 2024):\n",
    "    ds = xr.open_dataset(f'data/raw/phoenix/t2m_{year}.nc')\n",
    "    df = pd.DataFrame(index=ds['time'], data=ds['t2m'][:, 0, 0])\n",
    "    df.index = pd.to_datetime(df.index)\n",
    "    df_list.append(df)\n",
    "\n",
    "df = pd.concat(df_list)\n",
    "df.columns = ['Observed']\n",
    "\n",
    "# convert Kelvin to Fahrenheit\n",
    "df.Observed = (df.Observed - 273.15) * 9/5 + 32\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Zone Adjustment\n",
    "\n",
    "Change from UTC to local time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974-01-01 00:00:00</th>\n",
       "      <td>47.510187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01 01:00:00</th>\n",
       "      <td>45.625235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01 02:00:00</th>\n",
       "      <td>45.178172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01 03:00:00</th>\n",
       "      <td>43.958910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01 04:00:00</th>\n",
       "      <td>43.766911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Observed\n",
       "1974-01-01 00:00:00  47.510187\n",
       "1974-01-01 01:00:00  45.625235\n",
       "1974-01-01 02:00:00  45.178172\n",
       "1974-01-01 03:00:00  43.958910\n",
       "1974-01-01 04:00:00  43.766911"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mst = df.copy()\n",
    "df_mst.index = df_mst.index.shift(-7, freq='h')\n",
    "df_mst = df_mst.iloc[7:]\n",
    "df_mst.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Climate Adjustment\n",
    "\n",
    "The model I'm using requires the data to be stationary. So, I'm going to make a linear correction to the data to account for the upward trend of temperatures due to climate change. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07298165298829348"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the slope of the temperature trend across yearly averages\n",
    "\n",
    "df_yearly = df_mst.resample('YS').mean()\n",
    "slope = np.polyfit(df_yearly.index.year, df_yearly['Observed'], 1)[0]\n",
    "slope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A change in 0.7 degrees F per decade is quite high--about double the global temperature increase in recent years at about 0.35 degrees F per decade. However, this isn't shocking since different parts of the world are warming at different rates. This map produced by NOAA shows the temperature trend since 1994 (bottom plot), and, just eye-balling it, Arizona looks pretty close to 0.7 degrees per decade.\n",
    "\n",
    "![images/global-surface-temperature-trends-map_1901-2023_and_1994-2023_1400px_alt02.jpg](images/global-surface-temperature-trends-map_1901-2023_and_1994-2023_1400px_alt02.jpg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Observed</th>\n",
       "      <th>Climate Adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>438281.000000</td>\n",
       "      <td>438281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73.368176</td>\n",
       "      <td>75.083226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.317895</td>\n",
       "      <td>18.287642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.952607</td>\n",
       "      <td>24.262607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>58.229236</td>\n",
       "      <td>59.993846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73.569382</td>\n",
       "      <td>75.281794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>88.170412</td>\n",
       "      <td>89.848373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>119.622822</td>\n",
       "      <td>121.932822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Observed  Climate Adjusted\n",
       "count  438281.000000     438281.000000\n",
       "mean       73.368176         75.083226\n",
       "std        18.317895         18.287642\n",
       "min        21.952607         24.262607\n",
       "25%        58.229236         59.993846\n",
       "50%        73.569382         75.281794\n",
       "75%        88.170412         89.848373\n",
       "max       119.622822        121.932822"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add climate adjusted data to dataframe\n",
    "\n",
    "df_mst['Climate Adjusted'] = df_mst['Observed'] - 0.07 * (df_mst.index.year - 2024)\n",
    "df_mst.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Long-Term Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_yearly = df_mst.resample('YS').mean()\n",
    "df_yearly.plot()\n",
    "plt.title('Yearly Average Temperature in Phoenix')\n",
    "plt.ylabel('Temperature (°F)')\n",
    "plt.xlabel('Year')\n",
    "plt.gca().get_lines()[1].set_linestyle('--')\n",
    "\n",
    "# set the color of both lines to orange\n",
    "plt.gca().get_lines()[0].set_color('orange')\n",
    "plt.gca().get_lines()[1].set_color('red')\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig('images/long_term_trend.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mst.to_csv('data/processed/observed_time_series.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardize Data\n",
    "\n",
    "Apply scale and offset so data has a mean of zero and standard deviation of one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "offset:  75.08\n",
      "scale:   18.29\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    438281.000000\n",
       "mean          0.000176\n",
       "std           0.999871\n",
       "min          -2.778425\n",
       "25%          -0.824831\n",
       "50%           0.011033\n",
       "75%           0.807456\n",
       "max           2.561663\n",
       "Name: Climate Adjusted, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset = round(df_mst['Climate Adjusted'].mean(), 2)\n",
    "print('offset: ', offset)\n",
    "scale = round(df_mst['Climate Adjusted'].std(), 2)\n",
    "print('scale:  ', scale)\n",
    "dft = (df_mst['Climate Adjusted'] - offset) / scale\n",
    "dft.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export Data\n",
    "\n",
    "Make final preparations for data before it is fed into VAE model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1526</th>\n",
       "      <th>1527</th>\n",
       "      <th>1528</th>\n",
       "      <th>1529</th>\n",
       "      <th>1530</th>\n",
       "      <th>1531</th>\n",
       "      <th>1532</th>\n",
       "      <th>1533</th>\n",
       "      <th>1534</th>\n",
       "      <th>1535</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-07-31</th>\n",
       "      <td>0.964283</td>\n",
       "      <td>0.758682</td>\n",
       "      <td>0.677235</td>\n",
       "      <td>0.667352</td>\n",
       "      <td>0.664610</td>\n",
       "      <td>0.607691</td>\n",
       "      <td>0.600477</td>\n",
       "      <td>0.801750</td>\n",
       "      <td>1.029786</td>\n",
       "      <td>1.009659</td>\n",
       "      <td>...</td>\n",
       "      <td>1.320080</td>\n",
       "      <td>1.315175</td>\n",
       "      <td>1.319647</td>\n",
       "      <td>1.315680</td>\n",
       "      <td>1.320585</td>\n",
       "      <td>0.886659</td>\n",
       "      <td>0.720375</td>\n",
       "      <td>0.695270</td>\n",
       "      <td>0.405120</td>\n",
       "      <td>0.365732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-19</th>\n",
       "      <td>0.703717</td>\n",
       "      <td>0.624205</td>\n",
       "      <td>0.521373</td>\n",
       "      <td>0.234700</td>\n",
       "      <td>0.337318</td>\n",
       "      <td>0.317850</td>\n",
       "      <td>0.227569</td>\n",
       "      <td>0.316138</td>\n",
       "      <td>0.638538</td>\n",
       "      <td>0.848409</td>\n",
       "      <td>...</td>\n",
       "      <td>0.345234</td>\n",
       "      <td>0.439151</td>\n",
       "      <td>0.485718</td>\n",
       "      <td>0.474878</td>\n",
       "      <td>0.369622</td>\n",
       "      <td>0.182857</td>\n",
       "      <td>0.012565</td>\n",
       "      <td>-0.023234</td>\n",
       "      <td>-0.123142</td>\n",
       "      <td>-0.315327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-07-06</th>\n",
       "      <td>0.839117</td>\n",
       "      <td>0.792497</td>\n",
       "      <td>0.617997</td>\n",
       "      <td>0.504204</td>\n",
       "      <td>0.451051</td>\n",
       "      <td>0.428592</td>\n",
       "      <td>0.352912</td>\n",
       "      <td>0.317114</td>\n",
       "      <td>0.430974</td>\n",
       "      <td>0.341955</td>\n",
       "      <td>...</td>\n",
       "      <td>1.397530</td>\n",
       "      <td>1.493560</td>\n",
       "      <td>1.484917</td>\n",
       "      <td>1.475048</td>\n",
       "      <td>1.471033</td>\n",
       "      <td>1.442517</td>\n",
       "      <td>0.777592</td>\n",
       "      <td>0.770106</td>\n",
       "      <td>0.652979</td>\n",
       "      <td>0.604590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-16</th>\n",
       "      <td>1.240002</td>\n",
       "      <td>1.121574</td>\n",
       "      <td>0.926427</td>\n",
       "      <td>0.802889</td>\n",
       "      <td>0.745228</td>\n",
       "      <td>0.727412</td>\n",
       "      <td>0.694128</td>\n",
       "      <td>0.678177</td>\n",
       "      <td>0.874774</td>\n",
       "      <td>0.905918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.542830</td>\n",
       "      <td>0.585644</td>\n",
       "      <td>0.596969</td>\n",
       "      <td>0.592411</td>\n",
       "      <td>0.527708</td>\n",
       "      <td>0.450505</td>\n",
       "      <td>0.173873</td>\n",
       "      <td>0.086105</td>\n",
       "      <td>0.030448</td>\n",
       "      <td>-0.082456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-02-22</th>\n",
       "      <td>-1.260056</td>\n",
       "      <td>-1.367588</td>\n",
       "      <td>-1.481018</td>\n",
       "      <td>-1.603712</td>\n",
       "      <td>-1.679799</td>\n",
       "      <td>-1.740936</td>\n",
       "      <td>-1.751113</td>\n",
       "      <td>-1.717562</td>\n",
       "      <td>-1.681764</td>\n",
       "      <td>-1.422338</td>\n",
       "      <td>...</td>\n",
       "      <td>1.220573</td>\n",
       "      <td>1.273707</td>\n",
       "      <td>1.275041</td>\n",
       "      <td>1.271531</td>\n",
       "      <td>1.321508</td>\n",
       "      <td>1.103985</td>\n",
       "      <td>0.782790</td>\n",
       "      <td>0.710072</td>\n",
       "      <td>0.526452</td>\n",
       "      <td>0.478230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-11</th>\n",
       "      <td>-1.041151</td>\n",
       "      <td>-1.114603</td>\n",
       "      <td>-1.207998</td>\n",
       "      <td>-1.491556</td>\n",
       "      <td>-1.553934</td>\n",
       "      <td>-1.626629</td>\n",
       "      <td>-1.624703</td>\n",
       "      <td>-1.664318</td>\n",
       "      <td>-1.511225</td>\n",
       "      <td>-1.323540</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.347897</td>\n",
       "      <td>-0.262600</td>\n",
       "      <td>-0.278471</td>\n",
       "      <td>-0.271025</td>\n",
       "      <td>-0.301563</td>\n",
       "      <td>-0.449291</td>\n",
       "      <td>-0.787470</td>\n",
       "      <td>-0.864418</td>\n",
       "      <td>-0.977245</td>\n",
       "      <td>-1.024708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-06-11</th>\n",
       "      <td>0.594550</td>\n",
       "      <td>0.479838</td>\n",
       "      <td>0.578058</td>\n",
       "      <td>0.037281</td>\n",
       "      <td>0.049013</td>\n",
       "      <td>0.052298</td>\n",
       "      <td>0.092726</td>\n",
       "      <td>0.322419</td>\n",
       "      <td>0.608160</td>\n",
       "      <td>0.908048</td>\n",
       "      <td>...</td>\n",
       "      <td>1.630983</td>\n",
       "      <td>1.819980</td>\n",
       "      <td>1.831177</td>\n",
       "      <td>1.671009</td>\n",
       "      <td>1.470882</td>\n",
       "      <td>1.320167</td>\n",
       "      <td>1.211221</td>\n",
       "      <td>1.204450</td>\n",
       "      <td>1.112331</td>\n",
       "      <td>1.030068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-07-29</th>\n",
       "      <td>0.912226</td>\n",
       "      <td>0.799497</td>\n",
       "      <td>0.679402</td>\n",
       "      <td>0.623579</td>\n",
       "      <td>0.602223</td>\n",
       "      <td>0.649598</td>\n",
       "      <td>0.627093</td>\n",
       "      <td>0.768274</td>\n",
       "      <td>0.969063</td>\n",
       "      <td>1.039755</td>\n",
       "      <td>...</td>\n",
       "      <td>1.437210</td>\n",
       "      <td>1.436331</td>\n",
       "      <td>1.436737</td>\n",
       "      <td>1.391794</td>\n",
       "      <td>1.193303</td>\n",
       "      <td>0.664128</td>\n",
       "      <td>0.388931</td>\n",
       "      <td>0.401096</td>\n",
       "      <td>0.255995</td>\n",
       "      <td>0.245182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-24</th>\n",
       "      <td>-0.149637</td>\n",
       "      <td>-0.277243</td>\n",
       "      <td>-0.380983</td>\n",
       "      <td>-0.462618</td>\n",
       "      <td>-0.562222</td>\n",
       "      <td>-0.645824</td>\n",
       "      <td>-0.717425</td>\n",
       "      <td>-0.609413</td>\n",
       "      <td>-0.270531</td>\n",
       "      <td>0.006312</td>\n",
       "      <td>...</td>\n",
       "      <td>1.681876</td>\n",
       "      <td>1.736526</td>\n",
       "      <td>1.729271</td>\n",
       "      <td>1.759037</td>\n",
       "      <td>1.746629</td>\n",
       "      <td>1.683300</td>\n",
       "      <td>1.499417</td>\n",
       "      <td>1.391677</td>\n",
       "      <td>1.277088</td>\n",
       "      <td>1.138498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-11-16</th>\n",
       "      <td>-1.256365</td>\n",
       "      <td>-1.275372</td>\n",
       "      <td>-1.219826</td>\n",
       "      <td>-1.323414</td>\n",
       "      <td>-1.307423</td>\n",
       "      <td>-1.380713</td>\n",
       "      <td>-1.384570</td>\n",
       "      <td>-1.386534</td>\n",
       "      <td>-1.233081</td>\n",
       "      <td>-1.154040</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.578587</td>\n",
       "      <td>-0.699425</td>\n",
       "      <td>-0.720038</td>\n",
       "      <td>-0.764508</td>\n",
       "      <td>-0.745449</td>\n",
       "      <td>-0.926166</td>\n",
       "      <td>-1.133848</td>\n",
       "      <td>-1.118575</td>\n",
       "      <td>-1.187577</td>\n",
       "      <td>-1.197444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>285 rows × 1536 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0         1         2         3         4         5     \\\n",
       "1975-07-31  0.964283  0.758682  0.677235  0.667352  0.664610  0.607691   \n",
       "2017-08-19  0.703717  0.624205  0.521373  0.234700  0.337318  0.317850   \n",
       "2001-07-06  0.839117  0.792497  0.617997  0.504204  0.451051  0.428592   \n",
       "2010-08-16  1.240002  1.121574  0.926427  0.802889  0.745228  0.727412   \n",
       "1987-02-22 -1.260056 -1.367588 -1.481018 -1.603712 -1.679799 -1.740936   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2006-12-11 -1.041151 -1.114603 -1.207998 -1.491556 -1.553934 -1.626629   \n",
       "1986-06-11  0.594550  0.479838  0.578058  0.037281  0.049013  0.052298   \n",
       "1992-07-29  0.912226  0.799497  0.679402  0.623579  0.602223  0.649598   \n",
       "2021-04-24 -0.149637 -0.277243 -0.380983 -0.462618 -0.562222 -0.645824   \n",
       "1991-11-16 -1.256365 -1.275372 -1.219826 -1.323414 -1.307423 -1.380713   \n",
       "\n",
       "                6         7         8         9     ...      1526      1527  \\\n",
       "1975-07-31  0.600477  0.801750  1.029786  1.009659  ...  1.320080  1.315175   \n",
       "2017-08-19  0.227569  0.316138  0.638538  0.848409  ...  0.345234  0.439151   \n",
       "2001-07-06  0.352912  0.317114  0.430974  0.341955  ...  1.397530  1.493560   \n",
       "2010-08-16  0.694128  0.678177  0.874774  0.905918  ...  0.542830  0.585644   \n",
       "1987-02-22 -1.751113 -1.717562 -1.681764 -1.422338  ...  1.220573  1.273707   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2006-12-11 -1.624703 -1.664318 -1.511225 -1.323540  ... -0.347897 -0.262600   \n",
       "1986-06-11  0.092726  0.322419  0.608160  0.908048  ...  1.630983  1.819980   \n",
       "1992-07-29  0.627093  0.768274  0.969063  1.039755  ...  1.437210  1.436331   \n",
       "2021-04-24 -0.717425 -0.609413 -0.270531  0.006312  ...  1.681876  1.736526   \n",
       "1991-11-16 -1.384570 -1.386534 -1.233081 -1.154040  ... -0.578587 -0.699425   \n",
       "\n",
       "                1528      1529      1530      1531      1532      1533  \\\n",
       "1975-07-31  1.319647  1.315680  1.320585  0.886659  0.720375  0.695270   \n",
       "2017-08-19  0.485718  0.474878  0.369622  0.182857  0.012565 -0.023234   \n",
       "2001-07-06  1.484917  1.475048  1.471033  1.442517  0.777592  0.770106   \n",
       "2010-08-16  0.596969  0.592411  0.527708  0.450505  0.173873  0.086105   \n",
       "1987-02-22  1.275041  1.271531  1.321508  1.103985  0.782790  0.710072   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2006-12-11 -0.278471 -0.271025 -0.301563 -0.449291 -0.787470 -0.864418   \n",
       "1986-06-11  1.831177  1.671009  1.470882  1.320167  1.211221  1.204450   \n",
       "1992-07-29  1.436737  1.391794  1.193303  0.664128  0.388931  0.401096   \n",
       "2021-04-24  1.729271  1.759037  1.746629  1.683300  1.499417  1.391677   \n",
       "1991-11-16 -0.720038 -0.764508 -0.745449 -0.926166 -1.133848 -1.118575   \n",
       "\n",
       "                1534      1535  \n",
       "1975-07-31  0.405120  0.365732  \n",
       "2017-08-19 -0.123142 -0.315327  \n",
       "2001-07-06  0.652979  0.604590  \n",
       "2010-08-16  0.030448 -0.082456  \n",
       "1987-02-22  0.526452  0.478230  \n",
       "...              ...       ...  \n",
       "2006-12-11 -0.977245 -1.024708  \n",
       "1986-06-11  1.112331  1.030068  \n",
       "1992-07-29  0.255995  0.245182  \n",
       "2021-04-24  1.277088  1.138498  \n",
       "1991-11-16 -1.187577 -1.197444  \n",
       "\n",
       "[285 rows x 1536 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reshape dataframe to have 64*24 columns\n",
    "\n",
    "k = 64*24\n",
    "n = dft.shape[0] // k\n",
    "\n",
    "data = dft.iloc[:n*k].values.reshape(n, k)\n",
    "index = dft.index[:-k:k]\n",
    "\n",
    "dft_reshaped = pd.DataFrame(data=data, index=index)\n",
    "\n",
    "np.random.seed(42)\n",
    "# shuffle rows\n",
    "dft_reshaped = dft_reshaped.sample(frac=1)\n",
    "dft_reshaped.to_csv('data/processed/phoenix_64days.csv')\n",
    "\n",
    "dft_reshaped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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